| تعداد نشریات | 24 |
| تعداد شمارهها | 881 |
| تعداد مقالات | 7,867 |
| تعداد مشاهده مقاله | 14,699,823 |
| تعداد دریافت فایل اصل مقاله | 12,550,035 |
Temporal trends and prediction of bovine tuberculosis: a time series analysis in the North-East of Iran | ||
| Iranian Journal of Veterinary Research | ||
| مقاله 3، دوره 23، شماره 1 - شماره پیاپی 78، 2022، صفحه 12-17 اصل مقاله (382.57 K) | ||
| نوع مقاله: Full paper (Original article) | ||
| شناسه دیجیتال (DOI): 10.22099/ijvr.2021.39440.5727 | ||
| نویسندگان | ||
| N. Esmaeilzadeh1؛ A. Bahonar* 2؛ A. Rahimi Foroushani3؛ M. Nasehi4؛ K. Amiri5؛ M. A. R. Hadjzadeh6 | ||
| 1Department of Food Hygiene and Quality Control, Faculty of Veterinary Medicine, University of Tehran, Tehran, Iran; 2Department of Epidemiology, Faculty of Public Health, Mashhad University of Medical Science, Mashhad, Iran | ||
| 2Department of Food Hygiene and Quality Control, Faculty of Veterinary Medicine, University of Tehran, Tehran, Iran | ||
| 3Department of Epidemiology and Biostatistics, Faculty of Public Health, Tehran University of Medical Science, Tehran, Iran | ||
| 4Department of Epidemiology, Faculty of Public Health, Iran University of Medical Science, Tehran, Iran | ||
| 5Deputy of Bureau Health and Management of Animal Diseases, Veterinary Organization of Iran, Tehran, Iran | ||
| 6Department of Physiology, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran | ||
| چکیده | ||
| Background: Bovine tuberculosis (BTB) is a disease with high economic relevance. Aims: This study aimed to determine a fast alert surveillance system for bTB before the outbreak in the epidemic region of Iran. Methods: This cross-sectional study was conducted using the Auto-Regressive Integrated Moving Average (ARIMA) model for monthly bTB detections (reactors). These reactor cases result from the positive Tuberculin Purified Protein Derivative (PPD) test on cattle farms for the period between April 2007 and March 2019 in Razavi Khorasan province. Autocorrelation functions (ACF) and partial autocorrelation functions (PACF) plots were used to determine model parameters. The Akaike Information Criteria (AIC) were employed to select the best-fitted model. The root mean square error (RMSE) was applied for the evaluation of the models. Then, the best-fitted model was hired to predict the cases for 12 oncoming months. The data were analysed by STATA (ver. 14) software with a significant level at P≤0.05. Results: ARIMA (3, 0, 3) 12 was introduced as a recommended fitted model according to white noise residual test (Q=22.87 and P=0.98), lower AIC (541.85), and more precise model RMSE (1.50). However, the forecast values were more than the observed values. Conclusion: The application and interpretation of ARIMA models are straightforward, and may be used as immediate tools for monitoring systems. However, we proposed an Auto-Regressive Integrated Moving Average with Exogenous Input (ARIMAX) model with some measurable exotic factors such as economic fluctuations, climate changes, and pulmonary tuberculosis to introduce a more precise and accurate model for the fast alert surveillance system. | ||
| کلیدواژهها | ||
| Bovine tuberculosis؛ Forecasting؛ Surveillance system؛ Time series analysis | ||
| مراجع | ||
|
Abdollahzadeh, H; Rahmanian, V; Sharifi, N; Zahedi, R; Bajestani, MJN; GholianAval, M; Esmaeilzadeh, N; Hadjzadeh, M and Ahmadian, M (2021). Risks assessment of Adherence to non-pharmaceutical measures towards COVID-19 among residents of Mashhad in the North-East of Iran during the awful wave of the epidemic. J. Family Med. Prim. Care. 10: 3211-3218.
Akbarein, H; Bahonar, AR; Bokaie, S; Mosavar, N; Rahimi-Foroushani, A; Sharifi, H; Makenali, AS; Rokni, ND; Marhamati-Khameneh, B and Broumanfar, S (2014). Determinants of bovine tuberculosis in dairy farms covered by the tuberculin screening test: A herd level case control study. IJE., 10: 15-24.
Becketti, S (2013). Introduction to time series using Stata. Vol. 4905, College Station, TX: Stata Press. PP: 176-182.
Brockwell, PJ (2014). Time series analysis. Wiley StatsRef: Statistics Reference Online.
Carrique-Mas, JJ; Medley, GF and Green, LE (2008). Risks for bovine tuberculosis in British cattle farms restocked after the foot and mouth disease epidemic of 2001. Prev. Vet. Med., 84: 85-93.
Ciaravino, G; García-Saenz, A; Cabras, S; Allepuz, A; Casal, J; García-Bocanegra, I; De Koeijer, A; Gubbins, S; Sáez, JL; Cano-Terriza, D and Napp, S (2018). Assessing the variability in transmission of bovine tuberculosis within Spanish cattle herds. Epidemics. 23: 110-120.
Claridge, J; Diggle, P; McCann, CM; Mulcahy, G; Flynn, R; McNair, J; Strain, S; Welsh, M; Baylis, M and Williams, DJ (2012). Fasciola hepatica is associated with the failure to detect bovine tuberculosis in dairy cattle. Nat. Commun., 3: 1-8.
Cosivi, O; Grange, JM; Daborn, CJ; Raviglione, MC; Fujikura, T; Cousins, D; Robinson, RA; Huchzermeyer, HF; de Kantor, I and Meslin, FX (1998). Zoonotic tuberculosis due to Mycobacterium bovis in developing countries. Emerg. Infect. Dis., 4: 59.
Cunha, GBD; Luitgards-Moura, JF; Naves, ELM; Andrade, AO; Pereira, AA and Milagre, ST (2010). Use of an artificial neural network to predict the incidence of malaria in the city of Canta, state of Roraima. Rev. Soc. Bras. Med. Trop., 43: 567-570.
Esmaeilzadeh, N; Bahonar, A; Rahimi Foroushani, A; Nasehi, M and Shakeri, MT (2019). Which type of univariate forecasting methods is appropriate for prediction of tuberculosis cases in Razavi Khorasan Province? A need for surveillance and biosurveillance systems. JMM., 7: e96229.
Esmaeilzadeh, N; Shakeri, M; Esmaeilzadeh, M and Rahmanian, V (2020). ARIMA models forecasting the SARS-COV-2 in the Islamic Republic of Iran. Asian Pac. J. Trop. Dis., 13: 521.
Gharbi, M; Quenel, P; Gustave, J; Cassadou, S; La Ruche, G; Girdary, L and Marrama, L (2011). Time series analysis of dengue incidence in Guadeloupe, French West Indies: forecasting models using climate variables as predictors. BMC Infect. Dis., 11: 1-13.
Gordejo, FR and Vermeersch, JP (2006). Towards eradication of bovine tuberculosis in the European Union. Vet. Microbiol., 112: 101-109.
Guo, LC; Wu, W; Guo, JQ; Wang, P and Zhou, BS (2008). Appling grey swing model to predict the incidence trend of hemorrhagic fever with renal syndrome in Shenyang. J. China Med. Univ., 37: 839-842.
Inlamea, OF; Soares, P; Ikuta, CY; Heinemann, MB; Achá, SJ; Machado, A; Ferreira Neto, JS; Correia-Neves, M; and Rito, T (2020). Evolutionary analysis of Mycobacterium bovis genotypes across Africa suggests co-evolution with livestock and humans. PLoS Negl. Trop. Dis., 14: e0008081.
Li, Q; Guo, NN; Han, ZY; Zhang, YB; Qi, SX; Xu, YG; Wei, YM; Han, X; and Liu, YY (2012). Application of an autoregressive integrated moving average model for predicting the incidence of hemorrhagic fever with renal syndrome. Am. J. Trop. Med. Hyg., 87: 364-370.
Liu, Q; Liu, X; Jiang, B and Yang, W (2011). Forecasting incidence of hemorrhagic fever with renal syndrome in China using ARIMA model. BMC Infect. Dis., 11: 1-7.
LoBue, PA; Enarson, DA and Thoen, CO (2010). Tuberculosis in humans and animals: an overview [serialised article. Tuberculosis: a re-emerging disease in animals and humans. Number 1 in the series]. Int. J. Tuberc. Lung. Dis., 14: 1075-1078.
Lu, HM; Zeng, D and Chen, H (2009). Prospective infectious disease outbreak detection using Markov switching models. IEEE Trans. Knowl. Data. Eng., 22: 565-577.
Meng, XJ; Lindsay, DS and Sriranganathan, N (2009). Wild boars as sources for infectious diseases in livestock and humans. Philos. Trans. R. Soc. Lond. B, Biol. Sci., 364: 2697-2707.
Menzies, FD and Neill, SD (2000). Cattle-to-cattle transmission of bovine tuberculosis. Vet. J., 160: 92-106.
Michel, AL; Müller, B and Van Helden, PD (2010). Mycobacterium bovis at the animal-human interface: A problem, or not? Vet. Microbiol., 140: 371-381.
Moosazadeh, M; Khanjani, N; Nasehi, M and Bahrampour, A (2015). Predicting the incidence of smear positive tuberculosis cases in Iran using time series analysis. Iran. J. Public Health, 44: 1526-1534.
Moosazadeh, M; Nasehi, M; Bahrampour, A; Khanjani, N; Sharafi, S and Ahmadi, S (2014). Forecasting tuberculosis incidence in Iran using box-Jenkins models. Iran. Red. Crescent Med. J., 16: e11779.
Munyeme, M; Munang’andu, HM; Nambota, A; Muma, JB; Phiri, AM and Nalubamba, KS (2012). The nexus between bovine tuberculosis and fasciolosis infections in cattle of the Kafue Basin Ecosystem in Zambia: implications on abattoir surveillance. Vet. Med. Int., 2012.
Olsson, GE; Hjertqvist, M; Lundkvist, Å and Hörnfeldt, B (2009). Predicting high risk for human hantavirus infections, Sweden. Emerg. Infect. Dis., 15: 104-106.
Smith, NH; Gordon, SV; de la Rua-Domenech, R; Clifton-Hadley, RS and Hewinson, RG (2006). Bottlenecks and broomsticks: the molecular evolution of Mycobacterium bovis. Nat. Rev. Microbiol., 4: 670-681.
Tadayon, K; Mosavari, N and Feizabadi, MM (2013). An epidemiological perspective on bovine tuberculosis spotlighting facts and dilemmas in Iran, a historically zebu-dominant farming country. Iran J. Microbiol., 5: 1-13.
Torgerson, P and Torgerson, D (2009). Benefits of stemming bovine TB need to be demonstrated. Nature. 457: 657.
Wang, YJ; Zhao, TQ; Wang, P; Li, SQ; Huang, Z; Yang, GQ; Li XY; and Liu, B (2006). Applying linear regression statistical method to predict the epidemic of hemorrhagic fever with renal syndrome. Chin. J. Vec. Biol. Contr., 17: 333-334. Wongkoon, S; Jaroensutasinee, M and Jaroensutasinee, K (2012). Development of temporal modeling for prediction of dengue infection in Northeastern Thailand. Asian Pac. J. Trop. Med., 5: 249-252.
Wu, W; Guan, P; Guo, JQ and Zhou, BS (2008). Comparison of GM (1, 1) gray model and ARIMA model in forecasting the incidence of hemorrhagic fever with renal syndrome. J. China Med. Univ., 37: 52-55.
Wu, Z; Wu, W and Wang, P (2006). Prediction for incidence of hemorrhagic fever with renal syndrome with back propagation artificial neural network model. Chin. J. Vec. Biol. Contr., 17: 223.
Zheng, YL; Zhang, LP; Zhang, XL; Wang, K and Zheng, YJ (2015). Forecast model analysis for the morbidity of tuberculosis in Xinjiang, China. PloS One. 10: e0116832. | ||
|
آمار تعداد مشاهده مقاله: 889 تعداد دریافت فایل اصل مقاله: 902 |
||